Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of ""knowledge"" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intelligence: search and problem solving, methods of making proofs, and the use of knowledge in looking for a proof. There is also a discussion of how to use the knowledge system. The final chapter describes a popular expert system. It describes tools for building expert systems using an example based on Expert Systems-A Practical Introduction by P. Sell (Macmillian, 1985). This type of software is called an ""expert system shell."" This book was written as a textbook for undergraduate students covering only the basics but explaining as much detail as possible.
Inhalt
Preface
Reader's Guide
Chapter 1: What is Knowledge?
1.1 How Knowledge is Seen in Philosophy
1.2 Method in Natural Science
1.3 Knowledge in Artificial Intelligence
1.4 Factual Knowledge
1.5 Inference Knowledge
Summary
Key Words
Exercises
Chapter 2: Factual Knowledge and Its Organization
2.1 The Organization of Document Information
2.2 Organization of Factual Knowledge
2.3 Thesaurus
2.4 Storage Structure of Information
2.5 Information Retrieval
2.6 Mathematical Theory of Classification
Summary
Key Words
Exercises
Chapter 3: Inferential Knowledge and Problem-Solving (I)
3.1 State-Space Representation of a Problem
3.2 Search Tree
3.3 Programs for Game-Tree Search
3.4 Graph Search
3.5 Characteristics of Problem-Solving Using State-Representations
3.6 Discovery of an Algorithm
Summary
Key Words
Exercises
Chapter 4: Inferential Knowledge and Problem-Solving (II)
4.1 Use of Heuristic Knowledge
4.2 Finding a Solution by the Decomposition of a Problem
4.3 Blackboard Model
4.4 Knowledge as a Constraint
4.5 Mutual Model
Summary
Key Words
Exercises
Chapter 5: Inference Using Symbolic Logic
5.1 Expressions of Rules Using Symbolic Logic
5.2 Proof Using Forward and Backward Reasoning
5.3 Proof Using the Resolution Principle
5.4 Forms of Questions
5.5 Logical Representation of a Database
5.6 Inference in Changing Situations
5.7 Other Inference Methods
Summary
Key words
Exercises
Chapter 6: Knowledge Representation and Question-Answering
6.1 Semantic Networks
6.2 Frames and Scripts
6.3 Dialog Model
Summary
Key Words
Exercises
Chapter 7: Expert Systems
7.1 Features of Expert Systems
7.2 Production Systems
7.3 A Program for Forward Reasoning
7.4 A Program for Backward Reasoning
7.5 Reliability of Inference
7.6 Dialog with a User
7.7 Characteristics of an Expert System
Summary
Key Words
Exercises
Answers to Exercises
Recommended Books
Index